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Sumanto, Sumanto
Universitas Bina Sarana Informatika
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Combination of MOORA and ITARA Methods in Decision Support Systems for Measuring the Performance of Quality Control Teams Hendrastuty, Nirwana; Wang, Junhai; Sulistiyawati, Ari; Darwis, Dedi; Setiawansyah, Setiawansyah; Jumaryadi, Yuwan; Sumanto, Sumanto
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8382

Abstract

The problems that often arise in evaluating the performance of the Quality Control team are the subjectivity in determining the weight of criteria and the limitations of traditional methods in producing objective and consistent rankings. To address this issue, this research integrates the Indifference Threshold-based Attribute Ratio Analysis (ITARA) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods within a decision support system. The ITARA method is used to determine the weights of criteria based on data variation, making them more representative of real conditions, with the result that Accuracy of Product Defect Identification becomes the most dominant criterion with a weight of 0.3999, followed by Response Speed to Issues at 0.1877, while other criteria have lower weights. Furthermore, the MOORA method is used to calculate the preference of alternatives, resulting in a final ranking. The analysis results indicate that the Quality Assurance Team ranks first, followed by the Quality Improvement Team in second place, while the Quality Inspection Team is in the last position. To test the reliability of the model, a sensitivity analysis was conducted by varying the weights of the main criteria. The results show that the ranking structure is relatively stable, with changes only occurring in the positions of the first and second ranks when the accuracy weight is reduced by 0.2. In conclusion, the combination of ITARA-MOORA proves to be capable of producing objective, robust, and reliable performance evaluations as a basis for strategic decision-making in enhancing the quality of the quality control teams.
Komparasi Algoritma Machine Learning (SVM, Random Forest, dan Regresi Logistik) untuk Prediksi Tingkat Obesitas Achmad Rivai Syahputra; Rian Hidayat; Fathur Rismansyah; Sumanto Sumanto; Imam Budiawan; Roida Pakpahan
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1716

Abstract

Obesity is a global health issue with a continuously increasing prevalence. Early prediction of obesity levels is crucial for designing more effective intervention strategies. This study aims to apply and analyze the performance of three machine learning classification methods: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR), for predicting obesity levels. The research methodology utilizes a public dataset, ObesityLevels, downloaded from the Kaggle platform, which consists of 2111 medical and lifestyle records. The process includes data preprocessing to convert categorical features into numerical ones, splitting the data into training and testing sets with a 70:30 ratio, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the Random Forest (RF) algorithm achieved the highest performance, with an accuracy of 90.3%, precision of 90.3%, recall of 90.3%, and an F1-score of 90.3%. Based on these findings, it is concluded that the Random Forest model is the most effective choice for an obesity level prediction system based on the dataset used.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Faruk Ulum; Junhai Wang; Dyah Ayu Megawaty; Ari Sulistiyawati; Riska Aryanti; Sumanto Sumanto; Setiawansyah Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Perbandingan Hasil Klasifikasi Decision Tree dan Naïve Bayes dalam Memprediksi Churn Nasabah Bank Rizal Maulana; Fardha Hasykir; Muhammad Furqon Prasetyo; Rafi Kurniawan; Sumanto Sumanto; Andi Diah Kuswanto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9218

Abstract

Abstrak - Nasabah bank adalah individu  yang memiliki hubungan keuangan dengan bank, seperti simpanan, pinjaman, atau layanan lainnya. Fenomena Churn menjadi perhatian penting karena dapat mempengaruhi pendapatan dan stabilitas lembaga perbankan. Penelitian ini bertujuan untuk memprediksi churn nasabah menggunakan algoritma machine learning Decision Tree dan Naive Bayes. Model ini dianalisis untuk menentukan tingkat AUC (Area Under The Curve), CA (Classification Accuracy), dan F1 Score, serta menilai efektivitasnya dalam kategori klasifikasi.Hasil penelitian menunjukkan bahwa algoritma Decision Tree mampu mencapai tingkat akurasi yang cukup baik, dengan nilai accuracy (CA) sebesar 85.4% sedangkan Naïve Bayes memiliki nilai accuracy sebesar 84.5%.  Nilai akurasi ini menunjukkan bahwa Decision Tree berada dalam kategori Good Classification dan dapat digunakan sebagai alat yang handal dalam mengidentifikasi nasabah yang berisiko churn. Temuan ini mendukung potensi penerapan machine learning dalam strategi retensi pelanggan di sektor perbankan. Studi ini juga membuka peluang untuk pengembangan lebih lanjut, termasuk integrasi dengan metode klasifikasi lain atau pemanfaatan teknik seleksi fitur untuk meningkatkan akurasi prediksi churn.Kata kunci: Naive Bayes; Decision Tree; Klasifikasi; Churn; Nasabah Bank Abstract - Bank customers are individuals who have financial relationships with banks, such as deposits, loans, or other services. Churn phenomenon is an important concern because it can affect the income and stability of banking institutions. This research aims to predict customer churn using Decision Tree and Naive Bayes machine learning algorithms. The model is analyzed to determine the level of AUC (Area Under The Curve), CA (Classification Accuracy), and F1 Score, as well as assess its effectiveness in the classification category. The results show that the Decision Tree algorithm is able to achieve a fairly good level of accuracy, with an accuracy value (CA) of 85.4% while Naïve Bayes has an accuracy value of 84.5%. These accuracy values indicate that Decision Tree is in the Good Classification category and can be used as a reliable tool in identifying customers at risk of churn. These findings support the potential application of machine learning in customer retention strategies in the banking sector. This study also opens up opportunities for further development, including integration with other classification methods or utilization of feature selection techniques to improve churn prediction accuracy.Keywords: Naïve Bayes; Decision Tree; Classification; Churn; Bank Customers
Prediksi Pertumbuhan Penduduk Kota Jakarta Timur Menggunakan Metode Regresi Linear Anita Adelia Syahfitri; Sumanto Sumanto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9431

Abstract

Abstrak - Pertumbuhan penduduk merupakan fenomena alami yang ditandai dengan peningkatan atau penurunan jumlah populasi di suatu wilayah. Sebagai wilayah dengan distribusi penduduk tertinggi di Provinsi DKI Jakarta, Kota Jakarta Timur menghadapi tantangan dalam penyediaan infrastruktur publik serta pengelolaan sumber daya akibat tingginya tingkat pertumbuhan penduduk. Penelitian ini bertujuan untuk melakukan prediksi terhadap jumlah penduduk Kota Jakarta Timur di masa mendatang menggunakan metode regresi linear sederhana. Data yang digunakan berupa data deret waktu (time series) jumlah penduduk pada periode 2010 hingga 2024. Hasil penelitian menunjukkan bahwa model regresi linear memiliki tingkat akurasi sangat baik dengan R-Squared sebesar 0,956. Nilai ini menunjukkan 95,6% variasi jumlah penduduk dapat dijelaskan oleh variabel tahun. Selain itu, hasil uji hipotesis menunjukkan variabel tahun memiliki pengaruh signifikan terhadap pertumbuhan jumlah penduduk di Kota Jakarta Timur.Kata Kunci: Pertumbuhan Penduduk; Prediksi; Regresi Linear; Jakarta Timur;  Abstract - Population change is a natural occurrence reflected in the rising or declining number of inhabitants within a specific area. As the area with the highest population concentration in DKI Jakarta Province, East Jakarta experiences notable challenges in delivering adequate public facilities and managing regional resources, primarily driven by its rapid demographic expansion. This research is conducted to forecast the future population of East Jakarta using a simple linear regression technique. The dataset consists of time series records on the number of residents spanning from 2010 to 2024. The findings reveal that the applied regression model achieves a high prediction accuracy, with an R-Squared value of 0,956. This indicates that 95,6% of the variability in population figures can be attributed to the year variable. Moreover, hypothesis testing confirms that the time variable significantly influences that increase in population size in East Jakarta.Keywords: Population Growth; Prediction; Linear Regression; East Jakarta;
SISTEM PERINGATAN DINI KANTUK PENGEMUDI MENGGUNAKAN MODEL YOLOV11N BERBASIS CITRA WAJAH Adi Supriyatna; Deny Kurniawan; Mochamad Wahyudi; Lise Pujiastuti; Sumanto Sumanto; Dedi Triyanto
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.732

Abstract

Kecelakaan lalu lintas akibat kantuk saat mengemudi merupakan salah satu penyebab utama kematian di jalan raya dan menjadi isu keselamatan yang krusial. Studi menunjukkan bahwa 20–30% kecelakaan disebabkan oleh pengemudi yang mengantuk, sehingga diperlukan sistem peringatan dini yang mampu mendeteksi kondisi ini secara akurat dan real-time. Penelitian ini bertujuan untuk mengembangkan model deteksi kantuk berbasis visi komputer menggunakan algoritma YOLOv11n, yang dikenal sebagai varian ringan dan cepat dari keluarga YOLO. Model dilatih menggunakan dataset citra wajah yang telah diproses dan diaugmentasi melalui platform Roboflow, dengan tujuan untuk mendeteksi tanda-tanda kantuk secara visual. Hasil evaluasi model menunjukkan performa yang sangat baik, dengan nilai mAP50 sebesar 0,9710 dan mAP50-95 sebesar 0,6796. Selain itu, precision mencapai 0,9382 dan recall sebesar 0,9280, yang mengindikasikan kemampuan deteksi yang tinggi serta tingkat kesalahan yang rendah. Temuan ini membuktikan bahwa YOLOv11n dapat diimplementasikan secara efektif dalam sistem peringatan dini untuk meningkatkan keselamatan pengemudi, bahkan pada perangkat dengan sumber daya terbatas. Penelitian ini tidak hanya menjawab tantangan efisiensi dan akurasi deteksi kantuk, tetapi juga memberikan kontribusi nyata bagi pengembangan sistem keselamatan kendaraan berbasis kecerdasan buatan. Ke depan, pengembangan sistem deteksi multimodal yang menggabungkan citra wajah dengan data fisiologis seperti EOG dan detak kepala disarankan untuk meningkatkan keandalan sistem dalam kondisi nyata.
KOMPARASI ALGORITMA K-NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, DAN NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN JERUK Deny Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti; Sumanto Sumanto; indra Chaidir
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.751

Abstract

Jeruk merupakan salah satu buah tropis yang banyak dikonsumsi masyarakat karena kandungan nutrisinya yang tinggi, khususnya vitamin C. Namun, produksi jeruk kerap mengalami penurunan akibat serangan penyakit, terutama pada bagian daun. Identifikasi penyakit secara manual dinilai kurang efisien dan rawan kesalahan, sehingga diperlukan sistem otomatis berbasis machine learning untuk membantu proses deteksi secara cepat dan akurat. Penelitian ini bertujuan untuk membandingkan tiga algoritma klasifikasi K-Nearest Neighbor (KNN), Support Vector Machine (SVM), dan Neural Network (NN) dalam mengidentifikasi penyakit daun jeruk berdasarkan fitur tekstur. Dataset yang digunakan terdiri dari lima kategori: Black Spot, Canker, Greening, Melanose, dan Healthy, dengan total 609 citra daun yang dibagi secara proporsional untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan bahwa model Neural Network memberikan performa terbaik dengan akurasi 87,5%, diikuti oleh SVM sebesar 82,4%, dan KNN sebesar 77,5%. Penelitian ini menunjukkan bahwa pendekatan machine learning, khususnya Neural Network, efektif dalam klasifikasi penyakit daun jeruk dan berpotensi untuk diimplementasikan lebih lanjut dalam bentuk aplikasi praktis bagi petani.
Decision Support System for Determining Strategic Warehouse Locations Using a Combination of the WENSLO Weighting and RAWEC Method Junhai Wang; Setiawansyah Setiawansyah; Temi Ardiansah; Faruk Ulum; Sumanto Sumanto
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1456

Abstract

Determining the location of a strategic warehouse is a crucial decision in supply chain management as it directly affects distribution efficiency, logistics costs, and service levels. This problem is multi-criteria and complex, requiring an approach that can accommodate differences in the importance of criteria as well as variations in performance among alternatives objectively. This study aims to develop a Decision Support System to determine a strategic warehouse location by combining the Weights by Envelope and Slope (WENSLO) weighting method and the Ranking of Alternatives with Weights of Criterion (RAWEC) ranking method. The WENSLO method is used to generate criteria weights based on the nonlinear strength of each criterion, while the RAWEC method is applied to calculate the final values and determine the ranking of warehouse location alternatives. A case study was conducted on eleven alternative locations with the main criteria including location cost, accessibility, safety, distribution travel time, and proximity to suppliers. The study results showed that Location TR obtained the highest final score of 0.9673 and was designated as the top priority warehouse location, followed by Location RD with a score of 0.6235 and Location HO with a score of 0.338, while Location QC had the lowest score of −0.975. These findings demonstrate that the combination of the WENSLO and RAWEC methods can produce rankings that are objective, consistent, and easy to interpret, making them a reliable decision-support tool for determining strategic warehouse locations and potentially applicable to other logistics and distribution problems.
Analisis Pendeteksian dan Klasifikasi Produk di Lingkungan Supermarket Menggunakan Dataset Roboflow Yamani, Teuku Arrasy; Rofiqi, Ainur; Fauzan, Muhammad Indra; Sumanto, Sumanto; Taufiq, Ghofar; Kumalasari, Kumalasari
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 2 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i2.1468

Abstract

Kemajuan teknologi visi komputer telah memberikan kontribusi signifikan dalam bidang ritel, khususnya dalam pendeteksian dan klasifikasi produk di supermarket. Penelitian ini menganalisis kinerja model You Only Look Once (YOLO) dalam mengidentifikasi berbagai produk menggunakan dataset Roboflow yang berisi 1.200 citra dengan 10 kelas produk. Dataset mencakup variasi kondisi nyata, seperti perubahan pencahayaan, orientasi objek, serta kemunculan latar yang kompleks. Model dievaluasi menggunakan metrik precision, recall, dan mean Average Precision (mAP). Hasil menunjukkan bahwa YOLO mencapai mAP50 sebesar 0,95 dan mAP50–95 sebesar 0,89, menandakan akurasi deteksi yang tinggi. Sebagai kontribusi utama, penelitian ini membandingkan performa YOLO dengan arsitektur deteksi ringan seperti MobileNet-SSD, di mana YOLO menunjukkan hasil lebih stabil pada kondisi visual yang bervariasi. Temuan ini menegaskan bahwa YOLO efektif digunakan untuk otomatisasi inventori dan pemantauan stok di lingkungan ritel modern.Kata kunci: Deteksi objek, YOLO, Visi komputer, Klasifikasi produk supermarket, Dataset Roboflow.
Implementasi Lightweight Neural Network Berbasis YOLOv8n untuk Klasifikasi Sampah Real-Time Ali, Muhamad Hafis; Sulaiman, Sulaiman; Ardiyansyah, Rizqi; Sumanto, Sumanto; Taufiq, Ghofar; Kumalasari, Jefina Tri
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 10, No 1 (2026): SEMNAS RISTEK 2026
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v10i1.8895

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi sampah real-time yang efisien pada perangkat edge dengan memanfaatkan arsitektur Deep Learning ringan berbasis YOLOv8. Klasifikasi sampah otomatis merupakan solusi krusial dalam Smart Waste Management, namun model jaringan saraf tiruan yang kompleks sering terkendala oleh keterbatasan sumber daya komputasi pada perangkat IoT. Penelitian ini menerapkan model YOLOv8n (nano) menggunakan teknik Transfer Learning untuk menyeimbangkan akurasi deteksi dan efisiensi komputasi. Dataset yang digunakan bersumber dari repositori publik Roboflow sebanyak 1.123 citra yang telah melalui proses augmentasi. Hasil pelatihan selama 50 epoch menunjukkan performa impresif dengan nilai mean Average Precision (mAP@50) mencapai 0.995, Presisi 0.998, dan Recall 1.0. Selain itu, model memiliki ukuran file yang sangat ringkas (±6 MB) dengan kecepatan inferensi yang memadai untuk operasi real-time. Hasil penelitian ini membuktikan bahwa algoritma lightweight neural network mampu diimplementasikan secara efektif untuk mendukung sistem pemilahan sampah cerdas berbasis Computer Vision.
Co-Authors Abdurrachman, Qais Achmad Rivai Syahputra Ade Budiman, Ade Ade Christian Ade Christian Ade Christian Ade Christian, Ade Adi Pangestu Adi Supriyatna Aditia Yudhistira Agung Wibowo Agus Buono Ahmad Habibullah Ahmad Yani ahmad yani Ahmad Yani , Ahmad Yani Alamsyah, Muhammad Arkan Alghifar Firgiawan Alghiffary, Muhammad Adya Ali, Muhamad Hafis Ali, Satrio Nur Alwan Kapi Muntaha Alya Avisa Andi Diah Kuswanto Andika Amansyah Andri Amico Anggreani, Namira Anita Adelia Syahfitri Antony Pangaribuan, Rizky Daud Apip Supiandi Aprilyanto, Ryan Dwi Ardiyansyah, Rizqi Ari Sulistiyawati Ari Sulistiyawati Arnata Nur Rasyid Arshad, Muhammad Waqas Arya, Yudi Asmawati Asmawati Asy'ari, Muhammad Rifqi Audy Aulia Azzahra Aulia Rachmat, Daffa Azkia, Farah Diba Bib Paruhum Silalahi Bismo Raharjo, Yohanes Aryo Budhi Adhiani Budhi Adhiani Christina Budi Santoso Budiman, Ade Surya Cahya, Titus Dwi Cahyani Ayu Sulistyawati Christian , Ade Damayanti Damayanti Dedi Darwis Dedi Triyanto Dedi Triyanto DENY KURNIAWAN Deny Kurniawan Desiana Nuranudin Putri Dewi, Revinta Arrova Diah, Andi Dinda Aprillia Dyah Ayu Megawaty Dyani Kalyana Mitta Eka Dyah Setyaningsih Eka Putri Alvi Syahrina Elisabeth Sri Hendrastuti Erlangga Rizki Ekaptra Faatin, Safinah Fahrian Fahroni, Aldiwa Alfa Thira Nur Faiz Djarot, Raihan Jamal Fajar Akbar Fajar Yoga Adiansyah Fajrian, Ihsan Fardha Hasykir Farhan Fadhilah Faris Syahrendra Faruk Ulum Fathur Rismansyah Fauzan Nawwir Andriansyah Fauzan, Muhammad Indra Ganda Wijaya Ganda Wijaya, Ganda Ghofar Taufiq, Ghofar Ginting Wibi Prasetyo Gustian, Riansyah Hafis Nurdin Harianto Harianto Hariyanto HARIYANTO HARIYANTO Hartanti Hartanti Hernawan, Muhammad Hendra Hidayat, Manarul Hilmy Ibrahim, Farras Imam Budiawan Imam Budiawan Imam Wahyudi Indah Purwandani Indra Chaidir, Indra Indra, Ahmad Indriani , Karlena Indriyanti, Zahra Kiky Dwi Insani Abdi Bangsa Iqro Mukti Arto Jefina Tri Kumalasari Joseph Melchior Nababan Jumadi, Yakobus Linus Jumaryadi, Yuwan Junhai Wang Junhai Wang Kadir, Fauwas Abdul Kaisar Ages Querio Karlena Indriani Karlisa Priandana Karo-Karo, Julkarnaen Kevin Dwi Satria Kotjek, Rafie Kumalasari Kumalasari Kuswanto, Andi Diah Laksono, Andriansyah Tri Laura Gabriel da Silva Lia Mazia, Lia Lise Pujiastuti Lise Pujiastuti Lita Sari Marita Maharani Rona Makom Mantriwira, Daniel Mardinawat Mardinawat Mardinawati Mardinawati Marundrury, Aberahamo Onoma Megawaty, Dyah Ayu Mochamad Wahyudi Muhammad Furqon Prasetyo Muhammad Raviansyah Musfiroh Musfiroh, Musfiroh Nabilla, Adinda Naufal Hermawan, Rezan Nindya Dwi Lestari Nirwana Hendrastuty Noviyanto Nur Rachmat Nugraha Nurfia Oktaviani Syamsiah Nurrahman, Alvin Oprasto, Raditya Rimbawan Paduloh Paduloh Pakpahan, Roida Pasaribu, A. Ferico Octaviansyah Paulus Paulus Permata, Permata Prasetyo Adi Suwignyo Prasetyo, Romadhan Edy Pribadi, Denny Pricillia Primadana, Raihan Pujiastuti, Lise Putra Satria Putra, Imam Hanif Rachmat Adi Purnama Rafi Kurniawan Raihan Naufal Ramadhan Raihan Raihan, Raihan Ramadani, Achmes Dade Ramadhan, Muhammad Gilang Ramadhani, Dwiki Gilang Ramadhani, Varla Octavia Rani, Maulidina Cahaya Rasendriya, Rafi Ratiyah* Ratiyah Respati Putra, Micho Reynaldi , Reynaldi Rian Hidayat Rifda Ilahy Rosihan Rifki Nur Hidayat Putra Riska Aryanti Riska Aryanti Rivaldi, Muhammad Rizal Maulana Rizqi Ramadhani, Muhammad Rofiqi, Ainur Roida Pakpahan Roida Pakpahan Roni Saputra Pratama Ruhul Amin Ruli , Ahmad Rais Rumidjan Rumidjan, Rumidjan Rusda Wajhillah Ryan Randy Suryono Ryehan Alfiansyah Sanriomi Sintaro Saputra, Sabita Abigail Saputra, Yusup Sefriani, Shintia Putriayu Sentanu, Quinn Abrar Athallah Setiawan, Dandi Setiawansyah Setiawansyah Siregar, Denny Solihin Solihin Souisa, Juanny Cheristy Sri Hendrastuti, Elisabeth Sri Sugiharti Suci, Bintang Dyas SUKAMTI . Sulaiman Sulaiman Sumarna Sumarna Sumarna Sumarna Syakir, Adryan Raihan Tarmidzi Ibrahim Taufig, Ghofar Teguh Budhi Santosa Teguh Budi Santosa Temi Ardiansah Teuku Vaickal Rizki irdian Tito, Herdinan Tri Widian Ratnasari Ulum, Faruk Umam, Hairul Umar, Muhammad Hussein Ummu Radiyah, Ummu Vemi Januar Pratama Vera Agustina Yanti Virgiawan, Gilang Wahyudi, Agung Deni Wang, Junhai Wardani, Maidy Tri Wattilah, Florentina Widya Viona Septi Tanjung Wijaya, Filzah Wina Ningsih Yamani, Teuku Arrasy Yanuar Laik, Abraham Adrian Yunardus, Yunardus Yundari, Yundari Yuri Rahmanto Zahwa Asfa Rabbani Zalmi, Indah Oktavia Zidan, Muhammad `Diah Kuswanto, Andi